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Launching an automated microtiter cultivation platform for enhanced bioprocess optimization

18th April 2013 | Forschungszentrum Jülich GmbH IBG-1: Biotechnology Institute of Bio- and Geosciences (IBG) Bioprocesses & Bioanalytics Group Leo-Brandt-Str. / 52428 Jülich / Germany www.fz-juelich.de/ibg/ibg-1

P. Rohe, O. Schweissgut, R. Freudl, W. Wiechert, M. Oldiges

Folie 2

Outline

Introduction: What is so complicated about heterologous protein expression and why do we need higher troughput ? Results:

• JuBOS: Juelich Bioprocess Optimization System – A smart platform for small scale cultivation • Bioprocess optimization of C. glutamicum as expression system

− Strain selection − Induction profiling − Medium composition − Fed-Batch feedrate

Summary:

Slide 3

Replication

Transcription

Translation

Folding Secretion SEC-Path TAT-Path

Proteolysis Agglomeration

SP = signal peptide

Active protein

SP SP

Induction +

Affected by bacterial metabolism

Introduction: Optimizing protein production

Requires testing of different biological and bioprocess engineering variables

Slide 4

Hosts

Enzymes

Chaperones

Signal peptides

Temperature

Feedrate

Induction strength

Induction biomass

Media component

E. coli B. subtilis P. putida C. glutamicum R. capsulatus C. utilis

1 2 3 4 5 6 7 8 9 10 11 12 13

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Process phase Growth phase Production phase

680.400 com

binations 1.2*10

17

combinations

8*1022 com

binations

Enzyme Toolboxes (qualitative factors)

Bioengineering parameters (quantitative factors)

Introduction: Optimizing protein production

Slide 5

Outline

Introduction: What is so complicated about heterologous protein expression and why do we need higher troughput ? Results:

• JuBOS: Juelich Bioprocess Optimization System – A smart platform for small scale cultivation • Bioprocess optimization of C. glutamicum as expression system

− Strain selection − Induction profiling − Medium composition − Fed-Batch feedrate

Summary:

Slide 6

media preparation (Liquid handling)

cultivation (biolector)

storage (cooling device)

cell separation (mtp centrifuge)

photometric assay

(mtp reader)

Juelich Bioprocess Optimization System (JuBOS)

Integration of Cultivation and Robotic setup

• Installed in laminar flow cabinet • Automated media preparation • Pipetting (sampling/dosing/harvest)

• Programmable pipetting events triggered by (CDW, pO2, Time, pH) • Glucose fed-batch medium

Slide 7

Outline

Introduction: What is so complicated about heterologous protein expression and why do we need higher troughput ? Results:

• JuBOS: Juelich Bioprocess Optimization System – A smart platform for small scale cultivation • Bioprocess optimization of C. glutamicum as expression system

− Strain selection − Induction profiling − Medium composition − Fed-Batch feedrate

Summary:

Folie 8

Why is Corynebacterium glutamicum interesting ?

gram positive soil bacterium tempopt ≈ 30 °C / pHopt ≈ 7.2 discovered in Japan (1957) tolerates high glucose conc.

… is used for industrial amino acid production since many decades:

5 µm

L-Glutamate, L-Lysine, L-Iso-Leucine L-Leucine L-Valine …

… was also discovered for efficient protein secretion … very low extracellular protease background activity

Slide 9

Catalytic triad • Cutinase from Fusarium solani pisi • Lipase model-enzyme • Degrading cutin (insoluble matrix covering plant surface) • 22 kDa, no cofactors required • Application: Textile, Dairy, Detergents • Costs: 5000 €/g purified protein (www.vtt.fi)

• Library of 180 SEC- signal peptides (SP)

• Each SP is fused to cutinase

• Screening for best SP in MTP approach

SP Ranking: Brockmeyer 2006, J Mol Biol. 362(3):393-402

Target for Bioprocess development: Optimize secretory production of cutinase using C. glutamicum

Secretory cutinase production in B. subtilis

Transfer library of signal peptides for SEC transport to C. glutamicum Optimize secretory production of cutinase in C. glutamicum

Slide 10

Strain screening

media optimization

induction optimization

feedrate screening

feed optimi- zation

scale-up

clone- library

Premliminary tests

Increasing throughput via MTP-based cultivations similar to bioreactor conditions Optimization by modular „Step-by-step“ approach

Typical sequential steps during bioprocess optimization Application to C. glutamicum as expression system

Juelich Bioprocess Optimization System (JuBOS)

Slide 11

Time [h]0 2 4 6 8 10 12 14 16

CD

W [g

. L-1]

lip. a

ct. [

U. m

L-1]

02468

10121416

CDWlip. act.

Time [h]0 2 4 6 8 10 12 14 16

CD

W [g

. L-1]

lip. a

ct. [

U. m

L-1]

02468

10121416

CDWlip. act.

NprE YwmC YpjP Empty

spec

ific

activ

ity [U

. mg-1

]

0.00.20.40.60.81.01.21.4

NprE YwmC YpjP Empty

spec

ific

activ

ity [U

. mg-1

]

0.00.20.40.60.81.01.21.4

C.glutamicum ATCC 13032 pEKEX2::SP-Cutinase T=30°C, 1200 rpm, 3 mm, media: CG XII, 0.5 mM IPTG

CDW [mg.mL-1]0 2 4 6 8 10 12 14

lipol

ytic

act

ivity

[U. m

L-1]

02468

101214

1mL MTP-scale BioLector (NprE-Cutinase):

1.05 +/- 0.06 U.mg-1

µ = 0.4 h-1

µ = 0.4 h-1

CDW [mg.mL-1]0 2 4 6 8 10 12 14

lipol

ytic

act

ivity

[U. m

L-1]

02468

101214

1.07 +/- 0.03 U.mg-1 spec

.lip.

act.

[U. m

g-1 ]

spec

.lip.

act.

[U. m

g-1 ]

How comparable are results from 1 L lab-scale bioreactor and 1 mL MTP-scale BioLector cultivation ?

Perfect match of process characteristics with scale-up factor 1000 Similar µ, YX/S, YP/X and SP performance observed at MTP-scale

1L lab-scale Bioreactor (NprE-Cutinase): Rohe et al. Microbial Cell Factories 2012, 11:144.

(highly accessed)

Slide 12

time [h]

0 2 4 6 8 10 12 14 16 18

CDW

[g. L

-1]

02468

101214 sampling

time [h]

0 2 4 6 8 10 12 14 16 18

CDW

[g. L-1

]

02468

101214

time [h]

0 2 4 6 8 10 12 14 16 18

CDW

[g. L

-1]

02468

101214

4 h

start timer

sampling

4 h4 h

lip. a

ctiv

ity [U

. mL-

1 ]

02468

1012141618

epr amyE YwmC

lip. a

ctiv

ity [U

. mL-

1 ]

02468

1012141618

epr amyE YwmC

I. Synchronous sampling at 15 h (Over night culture):

C.glutamicum ATCC 13032 pXMJ 19: SP-Cutinase T=30°C, 1200 rpm, 3 mm, 1 mL media: CG XII, 0.5 mM IPTG

± 30 %

± 10 %

How to analyse fast and slow strains on a fair basis ?

II: Biomass triggered standardized sampling events

Strain specific standardization of individual growth/production time neccessary Biomass triggered sampling events improve reproducibility down to 10%

N > 11 technical replicates 3 different SP´s (epr, amyE, YwmC)

Slide 13

B. subtilis

C. glutam

icum

lip.act [U.mL-1]C.glutamicum

0 2 4 6 8 10 12

lip.a

ct. [

U. m

L-1 ]

B

.sub

tilis

0

2

4

6

lip.act [U.mL-1]C.glutamicum

0 2 4 6 8 10 12

lip.a

ct. [

U. m

L-1 ]

B

.sub

tilis

0

2

4

6

lip.act [U.mL-1]C.glutamicum

0 2 4 6 8 10 12

lip.a

ct. [

U. m

L-1 ]

B

.sub

tilis

0

2

4

6

lip.act [U.mL-1]C.glutamicum

0 2 4 6 8 10 12

lip.a

ct. [

U. m

L-1 ]

B

.sub

tilis

0

2

4

6

lip.act [U.mL-1]C.glutamicum

0 2 4 6 8 10 12

lip.a

ct. [

U. m

L-1 ]

B

.sub

tilis

0

1

2

3

4

5

6

7

- + ~equal

+ -

1

Y-Data: Brockmeyer 2006, J Mol Biol. 362(3):393-402 X-Data (this work): C.glutamicum ATCC 13032 pXMJ 19: SP-Cutinase 1 mL; T=30°C; 1200 rpm, 3 mm, CG XII, 0.5 mM IPTG

Good and bad signal peptides (SP): Comparing protein secretion performance in B. subtilis vs. C. glutamicum

Non-predictable secretion performance when transferring SP´s to C. glutamicum De-novo search for best SP seems to be obligatory !

Slide 14

CG XIIlip activity [U.mL-1]

0 2 4 6 8 10

BHI

lip a

ctiv

ity [U

. mL-1

]

0

1

2

3

4

5

Complex vs. Minimal medium

30°Clip activity [U.mL-1]

0 2 4 6 8 10

23°C

lip a

ctiv

ity [U

. mL-1

]

02468

101214

30°C vs. 23°C

Are good signal peptides always good ones : Uniqueness of the best SP (or unique only for chosen condition)

Complex vs minimal medium: only a few SP performance changes, but tendency for higher performance in minimal medium

Temperature change results in complete mix up of SP performance and no SP shows good performance under both conditions

SP´s are best suited only at chosen cultivation conditions !!

(com

plex

)

C.glutamicum ATCC 13032 , pXMJ 19: SP-Cutinase 1 mL, 1200 rpm, 3 mm, media: CG XII, 0.5 mM IPTG

Slide 15

Strain screening

media optimization

induction optimization

feedrate screening

feed optimi- zation

scale-up

clone- library

Premliminary tests

Results: media optimization

Juelich Bioprocess Optimization System (JuBOS)

Folie 16

IPTG induction: Optimal time point vs. concentration

Aktivität [U/mL]

Optimum clearly identified at 1 h and 200 µM IPTG

Optimization of time point of IPTG addition and induction strength for NprE-Cutinase (pEKEX2) in C. glutamicum with CGXII medium

IPTG was automatically added by the robotic setup and samples were harvested after cultivation

Mesh of 8 x 6 = 48 data points (= 1 MTP)

Slide 17

Strain screening

media optimization

induction optimization

feedrate screening

feed optimi- zation

scale-up

clone- library

Premliminary tests

What is the best minimal medium composition for C. glutamicum as expression system ?

Juelich Bioprocess Optimization System (JuBOS)

Slide 18

NH3 vs Harnstoff

Time [h]0 10 20 30

CD

W [g

. L-1]

0

5

10

15

ManualRobot

Glucose

Biotin

PCA Kanamycin

IPTG

FeSO4

MnSO4

ZnSO4

CuSO4

NiCl2

K2HPO4 / KH2PO4

(NH4)2SO4

CaCl2

MgSO4

MOPS

Urea

Betain

Volumes in .csv-Worklist

6 technical replicates

6 technical replicates

C.glutamicum DSM 13032 T = 30°C, 1 mL 1200 rpm, 3 mm, media: CG XII

Time for 1 mtp: 2.5 h

Time for 1 mtp: 15 min

What is the best minimal medium composition for C. glutamicum as expression system ?

Error: 10%

Error: 7%

Initial testing of all single components showed four interesting candidates Automated media optimization with Design of Experiments (DoE)

Med

ium

com

pone

nts

of C

GXI

I med

ium

Slide 19

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O4

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Ammonium sulfate [g.L-1]

0 0.

15

0.30

0.

45

0.60

copp

er s

ulfa

te [m

g.L-

1 ] Optimization of four parameter with evolutive response surface algorithm:

Evolutive Algorithm

Algorithm: Schweissgut & Wiechert, Proceedings of 7th EUROSIM Congress, Prague 2010

Medium optimization assisted by computational methods

BioLector experiment

Only 240 experiments from 625 were neccessary by using the evolutive response surface algorithm for DoE

Slide 20

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in

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in

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in

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0 0.

15

0.30

0.

45

0.60

copp

er s

ulfa

te [m

g.L-

1 ]

0.8 1.0 1.2 1.4 1.6

Classic media: CG XII

New media: CG ExXII

GFP

FL

(Glc

-lim

it, b

iom

ass

spec

.)

response surface function:

Medium optimization assisted by computational methods: Old CGXII and the new CGExXII

New CGExXII medium: factor 1.6 increased GFP fluorescence factor 1.5 increased cutinase activity

Slide 21

Strain screening

media optimization

induction optimization

feedrate screening

feed optimi- zation

scale-up

clone- library

Classical and MTP-based glucose feedrate optimization

Premliminary tests

Juelich Bioprocess Optimization System (JuBOS)

Slide 22

Start Feed

0

10

20

30

40

50

60

70

0 20 40 60

EA [k

U/L

]

CDW [g.L-1]

0

1

2

3

4

0 5 10 15

EAsp

ec [k

U/g

] Feed [gGlc

.L-1.h-1]

Start Feed

benchmark (batch)

DO

[%]

NprE-Cutinase

Classical feedrate optimization in 1 L bioreactor

Glucose feed rate is a critical design parameter for fed-batch Performance increase (3x) with optimal fed-batch setup

Different signal peptides showed different optimal fed-batch feedrates !

Slide 23

Strain screening

media optimization

induction optimization

feedrate screening

feed optimi- zation

Summary: Bioprocess optimization of cutinase expression in C. glutamicum using JuBOS

Premliminary tests

scale-up

14 days 4 days 16 days 4 days

240 cultivations

96 cultivations

384 cultivations

96 cultivations

Bioprocess optimization with 816 cultivations Possible with 1 person in ~6 weeks in MTP based cultivations

Juelich Bioprocess Optimization System (JuBOS)

Slide 24

Summary: Bioprocess optimization of cutinase expression in C. glutamicum using JuBOS

Strain screening

media optimization

induction optimization

feedrate screening

Premliminary tests

• Severe cross-relationship between different optimization modules oberved

• Initial strain screening conditions must be similar to production conditions

• Arrangement of the modules have direct effect on the final optimal set of biological and bioprocess parameters (local vs. global optimum) • Finding optimal standard conditions is a very unlikely scenario for protein secretion in C. glutamicum (also in other microbial systems ?) Strong demand for target protein specific de-novo optimization approach ! • JuBOS setup provide powerful technical environment for enhanced bioprocesss optimization under conditions very similar to lab-sale bioreactor conditions Outlook: • Implementation of parallelized fed-batch option and pH control in micro plate Strong demand for design of experiments

Folie 25

Acknowledgements

Forschungszentrum Jülich (IBG-1) : Prof. Bott Dr. Britta Kleine

Dr. Frank Kensy Dipl.-Ing. Carsten Müller

Project partners:

Heinrich-Heine University Düsseldorf: Prof. K.E. Jäger Prof. Pietruzska Dr. Ulrich Krauss Dr. Britta Kleine Kathrin Scholz

Project funding : Collaboration:

Mark your calendar: 15-19 July 2013, Berlin

#Quantitative Biology: Current concepts and tools for strain and process developments

www.QBio-SummerSchool.de

# Organized by former and current members of the Young Researchers Network (Zukunftsforum Biotechnologie) of the German Society for

Chemical Engineering and Biotechnology.